Hired Guns

The technology world would seem to have come a long way since the
first computer, as we would understand the term, was first
designed. In 1943, Howard Aiken and IBM created the first
universal calculator to calculate the data he needed to develop
his theory of space-charge conduction in vacuum tubes. Harvard
Mark I, as it was known, was 16m long and 2.4m high. It had over
765,000 parts, 3,300 relays, 175,000 connections and over 500
miles (800 km) of wire. At the time, the machine was unbelievably
fast: it could do 3 calculations per second.

Your first reaction to this may be slightly amusement and
possible smugness. After all, look where we are now, surely this
is ancient history.
But looking at today's trading technology, the challenges are
actually very similar. How do we find ways of processing more
data, more quickly? The volume of data continues to proliferate,
servers are getting bigger, but not by enough to satisfy the
inexhaustible craving for crunching all these figures. Every
trader creates his or her competitive advantage by being
microseconds faster than the competition. It is not only the
technology which is struggling to keep up. Perhaps more
significantly, it is a scarcity of skills with which to leverage
the technology which could mean that traders will lose
competitive advantage.
Barry Thompson, CEO and Founder of Tervela, says: "There has been
an explosion in algorithmic trading requirements since the start
of the economic downturn, as traders seek to change their
strategy. For example, hedging strategies which may have been
appropriate a few months ago are probably now too expensive and
erode profitability." Taking the point a step further, Dr Paul
Tolman, Founder, Beta Gamma Research, adds: "A successful trading
tool requires a variety of different skill sets - IT, quant
skills and trading experience. To be successful, you need to
bring these together, and it can be complicated to get the
combination to work. There is a skills shortage in some areas.
The larger investment banks can attract the right staff, but tier
two banks tend to struggle more, as they can't necessarily
justify the cost of a full time quant, for example."

Barry Thompson

A skills shortage? Thompson says: "There is significant demand
for algorithmic skills, both for financial modelling and
implementing trading strategies. While there is a dearth of quant
analysis skills, there is a greater dearth on the technical
side." But surely, there are thousands of graduates coming out of
college with well-sharpened programming pencils, so why should
there be a shortage?

Key to the problem is that automated trading requires highly
efficient programming to deal with massive volumes of records.
Any latency means that both time and competitive advantage tick
away. Simply adding new servers is not the answer. Simon Garland,
Chief Strategist at Kx predicts: "Servers will continue to get
bigger, and multiple core processing will evolve further, so it
is imperative to be able to programme in a multi-threaded
environment.

Banks and trading firms will not just be able to keep buying new
servers, however, systems need to be faster and use server
capacity optimally."

Dr Paul Tolman

First, adding new servers is expensive. Secondly, many firms have
stipulated that no new servers can be put in, unless others are
taken out, mindful that energy
costs have soared and power availability is finite. In London and
New York, for example, the problem of power is now acute. In
areas such as Canary Wharf, the risk of electricity "brown outs"
has become critical, which will be exacerbated as London's demand
for power increases ahead of the Olympic Games in 2012.
A key point which Simon Garland makes here is the issue of
multi-threaded processing. On a single processor, multithreading
involves the processor switching between different threads, or
tasks, storing the result of one process before moving on to the
next, and then switching back again. On a multiprocessor or
multi-core system, threads or tasks run concurrently, making
processes more efficient and increasing the sophistication and
speed with which calculations can be performed. True
multi-threading, using multiple processors, brings huge benefits
to automated trading, but only if banks, and the software
applications they are using, take advantage of them.

As Thompson emphasises: "While programmers are plentiful, the
individuals who can optimise a kernel [component for managing
system resources, among other functions], for example, are few
and far between." Having found the right skills, the problem then
is keeping them. As Tolman puts it: "Keeping hold of intellectual
property is a real challenge - once someone has come up with a
good strategy, it could be attractive for them to go to another
bank or set up a hedge fund. This is particularly an issue for
some of the smaller banks." So if the technical skills required
to support automated trading are not simply about developing
functionality and calculations, but also about minimising latency
and maximising the speed of processing, surely recent graduates
should have these skills? One of the issues, as Thompson
continues, is that: "College students graduating over the past 8
to 10 years have focused on the higher level programming
languages: Java, C# or C++. There are only a small group of
people who still count clock cycles and are able to look at the
most efficient way of developing particular functionality, rather
than simply the most efficient way of developing it in Java."

Simon Garland

There would seem to be two sets of people best equipped to deal
with this challenge, who at first sight might seem somewhat
incongruous. As Garland illustrates: "Ultimately what is
important is the ability to make programmes run extremely fast. A
group of people who have experience of doing this are those who
were programming in the 1970s and 1980s who appreciate the
importance of highly efficient code. Recent graduates are often
somewhat cavalier about CPU usage. However, if you're querying a
billion records, and retrieving 100,000 records, the process will
undoubtedly be slow if the programme is written badly and
multiple hits are made on the data, slower retrieval has a real
cost to the trader."

Secondly, perhaps rather than creating a vicious cycle of
continually poaching staff from proven development teams, there
is a case for looking beyond the traditional skills pot. Garland
continues: "A company we know has found it difficult to find the
right skills within the industry and have looked outside it to
the gaming industry. There are some definite advantages to taking
on people with this experience. They are accustomed to high speed
graphics and can optimise programmes to run extremely fast on
relatively slow machines. If you can take these skills and apply
them to the stock exchange, the result could be very exciting
indeed."

The idea may not seem as unlikely as it first appear. For
example, the skills requirement for a recently advertised job for
a racing game programmer read as follows:
• Strong math skills, including trigonometry, calculus and
linear algebra

• Experience with vehicle dynamics is preferred but by no
means essential.

• You will have been responsible for the physics
implementation in at least one completed title

• Strong C++ programming skills.

• Experience of racing games is preferred but is not
essential

• Strong written/verbal communication skills.

• Strong time management and organization skills.

• A passion for games and programming.

• A logical thinker with strong problem solving skills.

Of these, knowledge of vehicle dynamics and racing games may not
be critical to an automated trading tool programmer (although i
imagine that most programmers are hardly unfamiliar with whatever
the Wii or PS3 can throw at them) but most of the other skills
have very specific applicability to a trading environment. There
are other possible sources of new recruits too. As Vivake Gupta,
Lab49 explains: "We have had considerable success in finding
people not sullied by the financial services domain who come from
other environments. Those working in financial services for a
long time get used to things, and forget the inefficiencies that
they might have recognised to begin with. You need someone from
outside to have a clear view of where efficiencies can be
introduced."

One possible solution is to recruit specialists in military
technology. Someone working in missile defence probably
understands the imperative for rapid data analysis and
decision-making far better even than most algorithmic traders.
Similarly, the energy and aerospace sectors have the potential to
provide the talent and skills required for trading. Gupta
outlines other important skills: "Experts in data visualisation
can also be important to the trading environment. In the past,
there was little focus on the user interface. Someone experienced
with visual design can look at how traders use the system and
modify the user interface to make traders' role easier. This can
make a huge difference, not just in the way that data is
presented, but how it is used and what the sequence of tasks is.
While this does not increase the sophistication of system, it can
dramatically improve productivity and base level trading
ability."

Not everyone is convinced by the potential for sourcing skills
from outside the industry, however. Tolman warns: "While there
are certainly useful skills that can be sourced from further
afield, the problem with people from other industries is the lack
of practical trading knowledge. Teams need to be driven by the
needs of the trading desk, irrespective of where you source other
skills.

Vivake Gupta

Otherwise, it is easy to end up with something that either
doesn't work or is vulnerable to changes in market conditions." A
related problem is the length of time it takes for a recruit new
to the financial services industry to learn the trading
environment and become familiar with the vocabulary. It can take
months, or even years for a new recruit to reach their maximum
potential, which is a cost few organisations can support.

There are other issues, relating not to the background of
individuals in the trading solutions team, but the ability for
banks to dedicate the right investment to projects of this type
at all. Tolman explains: "In banks in particular, projects can
run for years with different IT and quant teams working together.
The result can end up being a huge monolithic system which is
very difficult to change and does not satisfy traders' needs.
Conversely, what traders need is a solution which is quickly
adaptable to market conditions. For example, recent increased
volatility and marked movements in FX rates have caught some
people out."

A question which management are asking increasingly is whether
there is a need to maintain these skills in-house at all, or
whether third parties could provide the necessary skills or
technology instead at a lower cost. The idea should not be an
anathema: in the 1980s, banks developed their own network
routers, today, such a project would seem absurd.
With skills at a premium and easily poached, a potentially long
learning curve for new recruits, and a far more rigorous approach
to the cost benefit of IT projects, now would seem to be the time
to look outside the firm to the tools which are available from
elsewhere. Many people I have interviewed have been pleasantly
surprised at what they have found.

The investment by specialist vendors in third party trading
applications, specialist programming tools and consultancies who
can optimise these tools vastly outweigh the investment that any
individual bank would be in a position to make. SunGard's recent
multi-million dollar acquisition of GLTrade, for example,
illustrates the growing strategic importance of automated and
algorithmic trading tools in the industry.

As Thompson summarises: "In the past, people used to build their
own trading software, which was specific to a particular
technical environment. Today, there is a movement towards
pre-packaged software and away from in-house development and we
expect to see a continuing trend in this direction."

The typical objections, that these solutions are not specific
enough to the organisation, that competitive advantage can only
be created by having in-house skills or that third party software
inhibits the variety of trading strategies that can be adopted
have long been disproved. Rather, high performance, the ability
to integrate market data seamlessly, the flexibility to implement
trading strategies quickly and greater accountability are all
characteristics not of in-house systems, but of modern, packaged
trading applications. Gupta emphasises: "Most people are focused
on their legacy system and it becomes impossible to innovate. The
fact that the financial services industry is not an early adopter
of new technology shows that there is a skills shortage. Banks
need to decide where to focus and what their core competencies
should be."

Not every trading firm is in a position to take immediate
advantage of third party systems, in which case looking for new
skills from beyond the normal channels could be an important way
of reinvigorating stale solutions. However, with budgets frozen
and every bank seeking to realign their strategies with their
core competencies, those who gain competitive advantage will be
the ones who can redirect their investment into financial
modelling and use their IT resources to implement and optimise,
rather than develop, the tools to turn these strategies into
reality.